通过试验-模型集成减少水稻产量对高浓度 CO2 响应的不确定性:华东案例研究

Zihao Wang, Yu Zhang, Xueni Wang, Yanfeng Ding, Songhan Wang
{"title":"通过试验-模型集成减少水稻产量对高浓度 CO2 响应的不确定性:华东案例研究","authors":"Zihao Wang, Yu Zhang, Xueni Wang, Yanfeng Ding, Songhan Wang","doi":"10.1016/j.cj.2024.06.012","DOIUrl":null,"url":null,"abstract":"Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO) concentrations. Estimates of CO fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.","PeriodicalId":501058,"journal":{"name":"The Crop Journal","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China\",\"authors\":\"Zihao Wang, Yu Zhang, Xueni Wang, Yanfeng Ding, Songhan Wang\",\"doi\":\"10.1016/j.cj.2024.06.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO) concentrations. Estimates of CO fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.\",\"PeriodicalId\":501058,\"journal\":{\"name\":\"The Crop Journal\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Crop Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cj.2024.06.012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Crop Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cj.2024.06.012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

要准确预测未来水稻的产量,需要精确估算水稻产量对气候变化因素的响应,其中最重要的是二氧化碳(CO)浓度的增加。然而,二氧化碳施肥效应(CFE)对水稻的估算仍存在较大的不确定性。因此,我们以华东地区的水稻种植区为研究区域,首先比较了四种最先进的作物模型预测的水稻产量和二氧化碳施肥效应,发现这些模型预测的二氧化碳施肥效应存在显著差异。然后,我们利用在江苏丹阳进行的田间对照试验,量化了 CFE 对水稻产量的影响。以田间试验的 CFE 测量值为基准,我们开发了一种试验-模型集成方法,旨在减少这种差异。因此,这项研究凸显了当前作物模型的巨大 CFE 不确定性,并为我们提供了减少这种不确定性的方法,这有利于在气候变化背景下准确预测未来全球水稻产量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China
Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO) concentrations. Estimates of CO fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信